First, a single fully-connected layer reduces the input spectrogram size to 512 output bins. Furthermore, batch normalization is applied to better handle the signals' dynamical range.
The essential element of Open-Unmix is its three-layer BLSTM network that enables to learn both long and short time dependencies [1]}.
The size of the output of the BLSTM is again reduced by a single connected layer, normalized, and upscaled to the output dimensions.
Finally, an element-wise multiplication of the input spectrograms with the estimated masks yields the final output to ensure that the network learns the ratio of the input belonging to the target source instead of the actual value.
